Simplify sampler and its error handling (#1441)

python/sglang/srt/sampling/sampling_batch_info.py

@@ -34,56 +34,6 @@ class SamplingBatchInfo:
     linear_penalties: torch.Tensor = None
     scaling_penalties: torch.Tensor = None
 
-    def __len__(self):
-        return len(self.temperatures)
-
-    def can_run_in_cuda_graph(self):
-        # Vocab bias and min_ps are not supported in CUDA graph
-        return (
-            self.logit_bias is None
-            and self.linear_penalties is None
-            and self.scaling_penalties is None
-            and not self.need_min_p_sampling
-        )
-
-    @classmethod
-    def dummy_one(cls, max_bs: int, vocab_size: int):
-        ret = cls(vocab_size=vocab_size)
-        with torch.device("cuda"):
-            ret.temperatures = torch.ones((max_bs, 1), dtype=torch.float)
-            ret.top_ps = torch.ones((max_bs,), dtype=torch.float)
-            ret.top_ks = torch.ones((max_bs,), dtype=torch.int)
-            ret.vocab_mask = torch.zeros((max_bs, vocab_size), dtype=torch.bool)
-        return ret
-
-    def __getitem__(self, key):
-        if isinstance(key, slice):
-            # NOTE:This method is only used in CUDA graph
-            assert self.can_run_in_cuda_graph()
-            return SamplingBatchInfo(
-                vocab_size=self.vocab_size,
-                temperatures=self.temperatures[key],
-                top_ps=self.top_ps[key],
-                top_ks=self.top_ks[key],
-                vocab_mask=self.vocab_mask[key],
-            )
-        else:
-            raise NotImplementedError
-
-    def inplace_assign(self, bs: int, other: SamplingBatchInfo):
-        # NOTE:This method is only used in CUDA graph
-        assert self.can_run_in_cuda_graph()
-
-        self.vocab_size = other.vocab_size
-        self.temperatures[:bs] = other.temperatures
-        self.top_ps[:bs] = other.top_ps
-        self.top_ks[:bs] = other.top_ks
-
-        if other.vocab_mask is None:
-            self.vocab_mask[:bs].fill_(False)
-        else:
-            self.vocab_mask[:bs] = other.vocab_mask
-
     @classmethod
     def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
         reqs = batch.reqs
@@ -130,6 +80,9 @@ class SamplingBatchInfo:
 
         return ret
 
+    def __len__(self):
+        return len(self.temperatures)
+
     def update_penalties(self):
         self.scaling_penalties = None
         self.linear_penalties = None